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- W2112768936 abstract "<para xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> We present theorems and an algorithm to find optimal or near-optimal “stochastic resonance” (SR) noise benefits for Neyman–Pearson hypothesis testing and for more general inequality-constrained signal detection problems. The optimal SR noise distribution is just the randomization of two noise realizations when the optimal noise exists for a single inequality constraint on the average cost. The theorems give necessary and sufficient conditions for the existence of such optimal SR noise in inequality-constrained signal detectors. There exists a sequence of noise variables whose detection performance limit is optimal when such noise does not exist. Another theorem gives sufficient conditions for SR noise benefits in Neyman–Pearson and other signal detection problems with inequality cost constraints. An upper bound limits the number of iterations that the algorithm requires to find near-optimal noise. The appendix presents the proofs of the main results. </para>" @default.
- W2112768936 created "2016-06-24" @default.
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- W2112768936 date "2009-05-01" @default.
- W2112768936 modified "2023-09-27" @default.
- W2112768936 title "Optimal Noise Benefits in Neyman–Pearson and Inequality-Constrained Statistical Signal Detection" @default.
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- W2112768936 doi "https://doi.org/10.1109/tsp.2009.2012893" @default.
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